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An efficient augmented Lagrangian method for support vector machine
Yinqiao Yan,
Qingna Li
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此作品的通讯作者
数学学院
Beijing Institute of Technology
科研成果
:
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同行评审
13
引用 （Scopus）
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探究 'An efficient augmented Lagrangian method for support vector machine' 的科研主题。它们共同构成独一无二的指纹。
分类
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Engineering
Support Vector Machine
100%
Lagrangian Method
100%
Newton's Method
66%
Computational Cost
33%
Convergent
33%
Regularization
33%
Jacobian matrix
33%
Loss Function
33%
Subproblem
33%
Computer Science
Support Vector Machine
100%
Newton's Method
66%
Machine Learning
33%
Computational Cost
33%
Linear Systems
33%
Regularization
33%
Support Vector
33%
Support Vector Regression
33%
Dual Problem
33%
Chemical Engineering
Support Vector Machine
100%
Learning System
33%
Linear Systems
33%
Mathematics
Loss Model
60%
Smooth Term
20%
Generalized Jacobian
20%